Application of Stochastic Multi-Objective Particle Swarm Optimization for Sustainable Flexible Pavement Design

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研究生：	黎清山 Thanh-Son Le
論文名稱：	Application of Stochastic Multi-Objective Particle Swarm Optimization for Sustainable Flexible Pavement Design Application of Stochastic Multi-Objective Particle Swarm Optimization for Sustainable Flexible Pavement Design
指導教授：	周瑞生 Jui-Sheng Chou
口試委員:	楊亦東 I-Tung Yang 黃榮堯 Rong-Yau Ethan Huang
學位類別：	碩士 Master
系所名稱：	工程學院 - 營建工程系 Department of Civil and Construction Engineering
論文出版年：	2010
畢業學年度：	98
語文別：	英文
論文頁數：	79
外文關鍵詞：	probabilistic simulation, risk analysis
相關次數：	點閱：203 下載：1
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上一筆

Nowadays, global warming, the phenomenon of increasing global surface temperatures, is one of the major concerns in environmental management and protection. All industries, especially, the construction industry, should focus on reducing the impacts of this phenomenon. As a result, engineers face with engaging the construction design that not only balances the conventional tradeoffs between project cost and project duration, but also considers the environmental sensitivities to yield sustainable designs. The aim of this paper is to apply stochastic multi-objective optimization to attain the sustainable design of flexible pavement. This design does not only minimize the construction cost and the project duration, but also the CEs simultaneously under project environment. In order to obtain this goal, a novel multi-objective optimization algorithm based on particle swarm intelligence is proposed and validated with testing problems. Subsequently this algorithm and Monte Carlo simulation are integrated to form a stochastic multi-objective optimization process. A pavement project is used herein to illustrate the case application of proposed model. The results of proposed optimization process are the effective tool for decision maker to choose the most appropriate pavement design.

Table of Contents

ABSTRACT	i
Acknowledgements	ii
Table of Contents	iv
List of Figures	vii
List of Tables	ix
Chapter 1 INTRODUCTION	1
1.1 Research background	1
1.2 Research objectives	5
Chapter 2 LITERATURE REVIEW	6
2.1 Multi-objectives optimization in construction	6
2.2 CEs estimation in construction	10
2.3 Monte Carlo simulation and particle swarm optimization	12
Chapter 3 METHODOLOGY	15
3.1 Methodology	15
3.2 Monte Carlo simulation	18
3.2.1 Creating model	19
3.2.2 Generate random variable	19
3.2.3 Running simulation and result analysis	22
3.3 Mathematical estimation models	23
3.3.1 Cost estimation model	23
3.3.2 Time estimation model	25
3.3.3 CEs estimation model	26
3.4 Simulation process	27
Chapter 4 PROPOSED MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION	29
4.1 Background of PSO	29
4.2 Constrained multi-objective PSO	31
4.2.1 Configuration of multi-objective optimization	31
4.2.2 Constraint handling	31
4.3 Proposed algorithm	33
4.3.1 Update the velocity and location	35
4.3.2 Update personal best, archive and global best	36
4.4 Experiment	37
4.4.1 Testing functions	37
4.4.2 Performance metrics	42
4.4.3 Experiment results	44
Chapter 5 FLEXIBLE PAVEMENT DESIGN	48
5.1 Procedure of designing flexible pavement	48
5.2 Technical requirement	50
5.3 Stochastic multi-objective optimization for flexible pavement design	52
Chapter 6 CASE STUDY	54
6.1 Background of case study	54
6.2 Design specification	54
6.3 Parameters of estimation models	55
6.3.1 Project duration parameters	55
6.3.2 CEs and cost estimation parameters	58
6.4 Tuning parameters for CDMOPSO	62
6.5 Analytical results	62
Chapter 7 CONCLUSION AND RECOMMENDATION	67
7.1 Conclusion	67
7.2 Future works and recommendation	68
Reference	69
Appendix A Matlab code of CDMOPSO	A-1
Appendix B Traffic investigation for Highway No. 10	B-1
Appendix C Testing results	C-1
Appendix D Distribution fitting for cost and CEs	D-1

List of Figures
Figure 1 1: 2006 sources of CEs	3
Figure 2 1: Preference-based multi-objective optimization procedure	7
Figure 2 2: Schematic of an ideal multi-objective optimization procedure	8
Figure 3 1: Research flow	17
Figure 3 2: Monte Carlo simulation procedure	20
Figure 3 3: Illustration of inverse method (a) Continuous case, (b) discrete case	21
Figure 3 4: A typical example of CPM network	26
Figure 3 5: Simulation process	28
Figure 4 1: Illustration of PSO	29
Figure 4 2: Boundary routine	36
Figure 4 3: Illustration of update gbest	37
Figure 4 4: Real Pareto optimal for BNH function	39
Figure 4 5: Real Pareto optimal for TNK function	39
Figure 4 6: Real Pareto optimal for SRN function	40
Figure 4 7: Real Pareto optimal for OSY function	41
Figure 4 8: Illustration of GD metric	43
Figure 4 9: Illustration of RV metric	44
Figure 5 1: A typical example of flexible pavement design	48
Figure 5 2: Conventional design procedure of flexible pavement	49
Figure 5 3: An example of determining the structural coefficient	51
Figure 5 4: Stochastic multi-objective optimization for sustainable flexible pavement design	53
Figure 6 1: Profile of case study	57
Figure 6 2: CPM network for case study	57
Figure 6 3: The optimization result	63

 
List of Tables
Table 3 1: CES conversion factors	27
Table 4 1: Pseudo code of CDMOPSO	34
Table 4 2: Experiment result for BNH function	45
Table 4 3: Experiment result for TNK function	46
Table 4 4: Experiment result for SRN function	47
Table 4 5: Experiment result for OSY function	47
Table 6 1: Characteristic of layers	54
Table 6 2: Design inputs	55
Table 6 3: Activities and unit time estimate	56
Table 6 4: Distribution of unit cost for each activity	59
Table 6 5: Distribution unit CEs for each activity	59
Table 6 6: Correlation matrix of unit costs	60
Table 6 7: Correlation matrix of unit CEs	60
Table 6 8: Unit time correlation matrix	61
Table 6 9: List of non-dominated solutions	64
Table 6 10: The compromised solution	65

                                

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